These data come from the 2016 CCES and allow interested students to model the individual correlates of the Trump vote in 2016. Code/analysis heavily indebted to a 2017 analysis I did on my blog (see references).
A data frame with 64600 observations on the following 21 variables.
a numeric vector, a unique identifier for the respondent as they first appear in the CCES data.
a character vector for the state in which the respondent resides
a numeric that equals 1 if the respondent voted says s/he voted for Trump in 2016.
a numeric vector for age that is roughly calculated as 2016 -
birthyr, as it's coded in the CCES data.
a numeric that equals 1 if the respondent is a woman
a numeric vector that equals 1 if the respondent says s/he has a college degree
a character vector for the race of the respondent
a numeric vector for the respondent's household income. Ranges from 1 (Less than $10,000) to 12 ($150,000 or more).
a numeric vector for the respondent's ideology on a liberal-conservative discrete scale. 1 = very liberal. 5 = very conservative.
a numeric vector for the respondent's partisanship on the familiar 1-7 scale. 1 = Strong Democrat. 7 = Strong Republican. Other party supporters (e.g. libertarians) are coded as NA.
a numeric vector for whether the respondent self-identifies as a born-again Christian.
a numeric vector for the importance of religion to the respondent. 1 = not at all important. 4 = very important.
a numeric vector for the extent of church attendance for the respondent. 1 = never. 6 = more than once a week.
a numeric vector for the frequency of prayer for the respondent. 1 = never. 7 = several times a day.
a numeric vector for how angry the respondent is that racism exists. 1 = strongly agree (i.e. is angry racism exists). 5 = strongly disagree.
a numeric vector for agreement with statement that white people have advantages over others in the U.S. 1 = strongly agree. 5 = strongly disagree.
a numeric vector for agreement with statement that the respondent fears other races. 1 = strongly disagree. 5 = strongly agree.
a numeric vector for agreement with statement that racism is rare in the U.S. 1 = strongly disagree. 5 = strongly agree.
a numeric vector that serves as a latent estimate for religiosity from the
prayerfreq variables. Higher values = more religiosity.
a numeric vector that serves as a latent estimate for cognitive racism. This is derived from the
a numeric vector that serves as a latent estimate for empathetic racism. This is derived from the
Cooperative Congressional Election Study, 2016
The latent estimates for religiosity, cognitive racism, and empathetic
racism come from a graded response model estimated in
mirt. The concepts of
"cognitive racism" and "empathetic racism" come from DeSante and Smith.